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Machine Learning for Decision Making

Amir Sani 1
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Strategic decision-making over valuable resources should consider risk-averse objectives. Many practical areas of application consider risk as central to decision- making. However, machine learning does not. As a result, research should provide insights and algorithms that endow machine learning with the ability to consider decision-theoretic risk. In particular, in estimating decision-theoretic risk on short dependent sequences generated from the most general possible class of processes for statistical inference and through decision-theoretic risk objectives in sequential decision-making. This thesis studies these two problems to provide principled algorithmic methods for considering decision-theoretic risk in machine learning. An algorithm with state-of-the-art performance is introduced for accurate estimation of risk statistics on the most general class of stationary–ergodic processes and risk-averse objectives are introduced in sequential decision-making (online learning) in both the stochastic multi-arm bandit setting and the adversarial full-information setting.
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Submitted on : Thursday, January 14, 2016 - 2:43:35 PM
Last modification on : Thursday, January 20, 2022 - 4:12:33 PM
Long-term archiving on: : Friday, November 11, 2016 - 5:54:47 AM


  • HAL Id : tel-01256178, version 1


Amir Sani. Machine Learning for Decision Making. Machine Learning [stat.ML]. Université de Lille 1, 2015. English. ⟨tel-01256178⟩



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